From the course: Advanced AI Analytics on AWS: Amazon Bedrock, Q, SageMaker Data Wrangler, and QuickSight

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Hands-on demo: Bedrock analytics with Rust

Hands-on demo: Bedrock analytics with Rust

From the course: Advanced AI Analytics on AWS: Amazon Bedrock, Q, SageMaker Data Wrangler, and QuickSight

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Hands-on demo: Bedrock analytics with Rust

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- [Instructor] An emerging area of using large language models is to outsource some of the analytics operations that an organization would do by using a foundational model. In this case, we're going to use Bedrock and we're going to use some analytics workflow. So this is more of a proof of concept just to get some of the end-to-end pipeline working. And if I take a look at the README file here, you can see that this is a project in Rust that calls into the Bedrock Foundation API, and bases a forecast on sales data from a CSV. So first we would need Rust, we would need AWS credentials configured, we need an active AWS account and also the AWS SDK. And then you would go through here and do a forecast based on this sales data. So let's go ahead and take a look at the sales data. Again, a very simple kind of intentionally contrived data for this demo. And then if we look at the Cargo.toml file, this is where we're able to pull up everything that we need for the project. So in here, we…

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